95527 20251113150202.0 doi 10.1371/journal.pcbi.1008035 sideral 119596 ART-2020-119596 eng Aleta, Alberto (orcid)0000-0002-1192-8707 Data-driven contact structures: From homogeneous mixing to multilayer networks 2020 Access copy available to the general public Unrestricted The modeling of the spreading of communicable diseases has experienced significant advances in the last two decades or so. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. Here, we bridge a current gap in the mathematical modeling of infectious diseases and develop a framework that allows to account simultaneously for both the connectivity of individuals and the age-structure of the population. We compare different scenarios, namely, i) the homogeneous mixing setting, ii) one in which only the social mixing is taken into account, iii) a setting that considers the connectivity of individuals alone, and finally, iv) a multilayer representation in which both the social mixing and the number of contacts are included in the model. We analytically show that the thresholds obtained for these four scenarios are different. In addition, we conduct extensive numerical simulations and conclude that heterogeneities in the contact network are important for a proper determination of the epidemic threshold, whereas the age-structure plays a bigger role beyond the onset of the outbreak. Altogether, when it comes to evaluate interventions such as vaccination, both sources of individual heterogeneity are important and should be concurrently considered. Our results also provide an indication of the errors incurred in situations in which one cannot access all needed information in terms of connectivity and age of the population. info:eu-repo/grantAgreement/ES/DGA/E36-17R info:eu-repo/grantAgreement/ES/MINECO-FEDER/FIS2017-87519-P info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 4.475 2020 MATHEMATICAL & COMPUTATIONAL BIOLOGY 8 / 58 = 0.138 2020 Q1 T1 BIOCHEMICAL RESEARCH METHODS 16 / 77 = 0.208 2020 Q1 T1 2.628 2020 Cellular and Molecular Neuroscience 2020 Q1 Computational Theory and Mathematics 2020 Q1 Ecology 2020 Q1 Molecular Biology 2020 Q1 Genetics 2020 Q1 Modeling and Simulation 2020 Q1 Ecology, Evolution, Behavior and Systematics 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ferraz de Arruda, G. Moreno, Yamir Universidad de Zaragoza (orcid)0000-0002-0895-1893 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 16, 7 (2020), e1008035 [16 pp.] PLoS Comput. Biol. PLOS COMPUTATIONAL BIOLOGY 1553-734X 1306297 http://zaguan.unizar.es/record/95527/files/texto_completo.pdf Versión publicada 471860 http://zaguan.unizar.es/record/95527/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:95527 articulos driver 2025-11-13-15:00:37 ARTICLE